Determine the use of the forecast Who needs the forecast? All organizations operate in the atmosphere of uncertainty. Decisions to be made affects future of the organization.
Select the items to be forecasted The item to be forecasted. Dependent variable to be studied.
Determine the time horizon of the forecast Short-range forecast Up to 1 year Purchasing, job scheduling, job assignments Medium-range forecast 1 year to 3 years Sales and production planning Long-range forecast 3+ years New product planning, research and development
Select Forecasting approach Qualitative Methods Used when situation is vague and little data exist New products New technology Involves intuition, experience
Quantitative Methods Used when situation is ‘stable’ and historical data exist Existing products Current technology Involves mathematical techniques
Data collection One of the most difficult and time consuming part of forecasting is the collection of valid and reliable data. Forecast can be no more accurate than the data on which it is based Data can be collected from- primary source and secondary source
Four criteria can be applied to the determination of whether the data will be useful- Data should be reliable and accurate Data should be relevant Data should be consistent Data should be timely
Data Reduction Since available data can be either too much or too less, data reduction is necessary. Decide which data is most complete, valid and reliable to increase data accuracy. Some times accurate data may be available but only in certain historic periods.
Exploring Time Series Data Patterns Horizontal pattern- When data observation fluctuate around a constant level or mean Trend pattern- When data observation grow or decline over an extended period of time Cyclic pattern- When data observation exhibits rises and falls that are not of a fixed period Seasonal Pattern- When data observation are influenced by seasonal factors.
Exploring Data Patterns with Auto correlation Analysis Autocorrelation is the correlation between a variable lagged one or more period itself. It is used to detect non randomness of data To identify an appropriate time series model if data is not random
Y= 1704/12 = 142 r1 = 843/1474 = .572
Select the forecasting model(s) The most prominently used models are: Exponential smoothing method with 1 or 2 variables. Regression Models Once the model has been judicially selected, its parameters are estimated for model fitting purposes.
Make the forecast Forecast is made for a particular period.
Forecast evaluation Comparing Forecast value with actual historical values.